Optical coherence tomography (OCT) angiography techniques such as optical microangiography (OMAG), speckle variance, phase variance, etc. use OCT systems to achieve the imaging of functional vascular network within microcirculatory tissue beds in vivo, without the use of exogenous contrast agents. One of the major challenges of OCT angiography for in vivo imaging is the longer acquisition times and hence the associated inevitable subject movement that causes motion artifacts in the final results. Eye motion can result in image artifacts and hence greatly reduces the clinical usability of the acquired data.
US Patent Publication No. 2013/0176532, hereby incorporated by reference, describes some methods for dealing with motion artifacts in OCT Angiography data including active tracking. Previous methods that are used to reduce the motion artifacts through post-processing B-scan registration have included pixel-level shifting and phase compensation. Pixel-level accuracy shifting is an approach based on intensity OCT-structural images. This is a cross-correlation method that calculates pixel shifts in z direction between two B-scans. After determining the motion displacements (or shifts), the frames are re-registered by directly shifting the structure images without considering the phase information in the OCT signals. The limitation of this method is that it is a pixel-based approach (i.e. pixel level accuracy) and hence it could falsely register a motion contrast if there are uncompensated sub-pixel level shifts. Phase compensation uses the phase information in the OCT signals. Because the displacement (or motion) between B-scans causes the change of phase in OCT signals, the phase signal can be used to compensate motion in the spectral interferograms (or OCT signal) as discussed by Braaf et al. This method requires prior information about system parameters, such as imaging depth. Most commercial systems only provide the structural or intensity information of the OCT data and will need to be modified to obtain phase information. In addition, this method demands substantial computing power because of the repeated use of Fourier transformation operations to convert the OCT data between frequency (or wavenumber) and time (or depth structure) domain.